OpenUCT :: Browsing by Author "Stewart, Theodor J"

Browsing by Author "Stewart, Theodor J"

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Open Access
Aiding Decision making for foodbank Cape Town
(2010) Blake, Timothy James; Stewart, Theodor J; Van Dyk, Esbeth
Open Access
A decision support system for sugarcane irrigation supply and demand management
(2017) Patel, Zubair; Stray, Jonas; Stewart, Theodor J
Commercial sugarcane farming requires large quantities of water to be delivered to the fields. Ideal irrigation schedules are produced indicating how much water to be supplied to fields considering multiple objectives in the farming process. Software packages do not fully account for the fact that the ideal irrigation schedule may not be met due to limitations in the water distribution network. This dissertation proposes the use of mathematical modelling to better understand water supply and demand management on a commercial sugarcane farm. Due to the complex nature of water stress on sugarcane, non-linearities occur in the model. A piecewise linear approximation is used to handle the non-linearity in the water allocation model and is solved in a commercial optimisation software package. A test data set is first used to exercise and evaluate the model performance, then to illustrate the practical applicability of the model, a commercial sized data set is used and analysed.
Open Access
Decision support systems for solving discrete multicriteria decision making problems
(1992) Van Dyk, Theron Van Zyl; Stewart, Theodor J
The aim of this study was the design and implementation of an interactive decision support system, assisting a single decision maker in reaching a satisfactory decision when faced by a multicriteria decision making problem. There are clearly two components involved in designing such a system, namely the concept of decision support systems (DSS) and the area of multicriteria decision making (MCDM). The multicriteria decision making environment as well as the definitions of the multicriteria decision making concepts used, are discussed in chapter 1. Chapter 2 gives a brief historical review on MCDM, highlighting the origins of some of the more well-known methods for solving MCDM problems. A detailed discussion of interactive decision making is also given. Chapter 3 is concerned with the DSS concept, including a historical review thereof, a framework for the design of a DSS, various development approaches as well as the components constituting a decision support system. In chapter 4, the possibility of integrating the two concepts, MCDM and DSS, are discussed. A detailed discussion of various methodologies for solving MCDM problems is given in chapter 5. Specific attention is given to identifying the methodologies to be implemented in the DSS. Chapter 6 can be seen as a theoretical description of the system developed, while Chapter 7 is concerned with the evaluation procedures used for testing the system. A final summary and concluding remarks are given in Chapter 8.
Open Access
Developing decision support for Foodbank South Africa's allocation system: an application of operational research techniques to aid decision-making at a not-for-profit organization
(2011) Watson, Neil Mark; Stewart, Theodor J; Scott, Leanne
There is a dearth of research on the application of hard Operational Research (OR) techniques (simulation, linear programming, goal programming, etc.) in determining optimal ordering, inventory and allocation policies for goods within distribution systems in developing countries. This study aims to assist decision making at a not-for-profit organization (NPO), Foodbank South Africa (FBSA), within its allocation system through a combined ‘soft-hard’ OR approach. Two problem-structuring tools (soft OR), Causal Mapping (CM) and Soft System Methodology’s Root Definitions (RDs), are used to structure the organization's goals (in order to gain a comprehensive understanding of the decision-context) and gain a better understanding of the ‘decision-issues’ in the allocation system at its Cape Town warehouse.
Open Access
Development and application of a multi-criteria decision-support framework for planning rural energy supply interventions in low-income households in South Africa
(2022) Dzenga, Bruce; Stewart, Theodor J; Hughes, Alison
Problems in the public policy decision-making environments are typically complex and continuously evolve. In a resource-constrained environment, several alternatives, criteria, and conflicting objectives must be considered. As a result, solutions to these types of problems cannot be modelled solely using single-criteria techniques. It has been observed that most techniques used to shape energy policy and planning either produce sub-optimal solutions or use strong assumptions about the preferences of decision-maker(s). This difficulty creates a compelling need to develop novel techniques that can handle several alternatives, multiple criteria and conflicting objectives to support public sector decision-making processes. First, the study presents a novel scenario-based multi-objective optimisation framework based on the augmented Chebychev goal programming (GP) technique linked to a value function for analysing a decision environment underlying energy choice among low-income households in isolated rural areas and informal urban settlements in South Africa. The framework developed includes a multi-objective optimisation technique that produced an approximation of a Pareto front linked to an a priori aggregation function and a value function to select the best alternatives. Second, the study used this model to demonstrate the benefits of applying the framework to a previously unknown subject in public policy: a dynamic multi-technology decision problem under uncertainty involving multiple stakeholders and conflicting objectives. The results obtained suggest that while it is cost-optimal to pursue electrification in conjunction with other short-term augmentation solutions to meet South Africa's universal electrification target, sustainable energy access rates among low-income households can be achieved by increasing the share of clean energy generation technologies in the energy mix. This study, therefore, challenges the South African government's position on pro-poor energy policies and an emphasis on grid-based electrification to increase energy access. Instead, the study calls for a portfolio-based intervention. The study advances interventions based on micro-grid electrification made up of solar photovoltaics (PV), solar with storage, combined cycle gas turbine (CCGT) and wind technologies combined with either bioethanol fuel or liquid petroleum gas (LPG). The study has demonstrated that the framework developed can benefit public sector decision-makers in providing a balanced regime of technical, financial, social, environmental, public health, political and economic aspects in the decision-making process for planning energy supply interventions for low-income households. The framework can be adapted to a wide range of energy access combinatorial problems and in countries grappling with similar energy access challenges.
Open Access
The development of an operational management procedure for the South African west coast rock lobster fishery
(1998) Johnston, Susan Joy; Butterworth, Doug S; Stewart, Theodor J
This thesis considers the development of an operational management procedure (OMP) to provide scientific recommendations for commercial TAC for the South African west coast rock lobster (Jasus lalandii) fishery. This fishery has been under considerable stress in recent years as a result of overfishing and low somatic growth rates. Present catch levels, less than 2000 MT, are substantially smaller than levels recorded in the past. The present biomass (above 75mm carapace length) is estimated to be only six percent of the pristine level. At the start of this research, no long-term management strategy for the resource existed. Neither was there any robust, tested, scientific method available for setting the annual TAC for the fishery, which resulted in a time-consuming and unsatisfactory scientific debate each year in developing a series of ad hoc TAC recommendations. The work presented in this thesis is thus aimed at answering two important questions. i) Can an adequate mathematical model be developed as a basis to simulate the resource and its associated fishery? ii) Can a self-correcting robust OMP be developed for the resource? The first phase of this thesis is the development of a size-structured population model of the resource and the associated fishery. A size-structured model is necessary as lobsters are difficult to age and hence most of the data collected are on a size basis. Furthermore, important management issues, such as the legal minimum size which has changed over time, require a model able to take size-structure into account. This model is fitted to a wide range of data from the fishery, including CPUE (catch-per-unit-effort) and catch-at-size information, by maximising a likelihood function. The model is shown to fit reasonably well to all data, and to provide biologically plausible estimates for its six estimable parameters.
Open Access
Discriminant analysis : a review of its application to the classificationof grape cultivars
(1989) Blignaut, Rennette Julia; Zucchini, Walter; Stewart, Theodor J
The aim of this study was to calculate a classification function for discriminating between five grape cultivars with a view to determine the cultivar of an unknown grape juice. In order to discriminate between the five grape cultivars various multivariate statistical techniques, such as principal component analysis, cluster analysis, correspondence analysis and discriminant analysis were applied. Discriminant analysis resulted in the most appropriate technique for the problem at hand and therefore an in depth study of this technique was undertaken. Discriminant analysis was the most appropriate technique for classifying these grape samples into distinct cultivars because this technique utilized prior information of population membership. This thesis is divided into two main sections. The first section (chapters 1 to 5) is a review on discriminant analysis, describing various aspects of this technique and matters related thereto. In the second section (chapter 6) the theories discussed in the first section are applied to the problem at hand. The results obtained when discriminating between the different grape cultivars are given. Chapter 1 gives a general introduction to the subject of discriminant analysis, including certain basic derivations used in this study. Two approaches to discriminant analysis are discussed in Chapter 2, namely the parametrical and non-parametrical approaches. In this review the emphasis is placed on the classical approach to discriminant analysis. Non-parametrical approaches such as the K-nearest neighbour technique, the kernel method and ranking are briefly discussed. Chapter 3 deals with estimating the probability of misclassification. In Chapter 4 variable selection techniques are discussed. Chapter 5 briefly deals with sequential and logistical discrimination techniques. The estimation of missing values is also discussed in this chapter. A final summary and conclusion is given in Chapter 7. Appendices A to D illustrate some of the obtained results from the practical analyses.
Open Access
An examination of heuristic algorithms for the travelling salesman problem
(1988) Höck, Barbar Katja; Stewart, Theodor J
The role of heuristics in combinatorial optimization is discussed. Published heuristics for the Travelling Salesman Problem (TSP) were reviewed and morphological boxes were used to develop new heuristics for the TSP. New and published heuristics were programmed for symmetric TSPs where the triangle inequality holds, and were tested on micro computer. The best of the quickest heuristics was the furthest insertion heuristic, finding tours 3 to 9% above the best known solutions (2 minutes for 100 nodes). Better results were found by longer running heuristics, e.g. the cheapest angle heuristic (CCAO), 0-6% above best (80 minutes for 100 nodes). The savings heuristic found the best results overall, but took more than 2 hours to complete. Of the new heuristics, the MST path algorithm at times improved on the results of the furthest insertion heuristic while taking the same time as the CCAO. The study indicated that there is little likelihood of improving on present methods unless a fundamental new approach is discovered. Finally a case study using TSP heuristics to aid the planning of grid surveys was described.
Open Access
Game-theoretic models for mergers and acquisitions
(1995) Van den Honert, Robin Charles; Stewart, Theodor J
This thesis examines the corporate merger process as a bargaining game, under the assumption that the two companies are essentially in conflict over the single issue of the price to be offered by the acquirer to the target. The first part of the thesis deals with the construction and testing of analytical game-theoretic models to explain the proportion of the synergy gains accruing to the target company under different assumptions about the players' a priori knowledge. Assuming full certainty amongst the players about the pre- and post-merger values of the companies, the distribution of gains between target and acquiring companies that would be consistent with the Nash-Kalai axioms is determined in principle. The resulting model depends on the players' utility functions, and is parameterised by the relative bargaining strength of the players and their risk aversion coefficients. An operational version of the model is fitted to empirical data from a set of 24 recent mergers of companies quoted on the Johannesburg Stock Exchange. The model is shown to have good predictive power within this data set. Under the more realistic assumption of shared uncertainty amongst the two players about the post-merger value of the combined company, a Nash-Kalai bargaining model incorporating this uncertainty is developed. This model is an improvement over those with complete certainty in that it offers improved model fit in terms of predicting the total amount paid by an acquirer, and is able to dichotomise this payment into a cash amount and a share transfer amount. The theoretical model produced some results of practical value. Firstly, a cash-only offer is never optimal. Conditions under which shares only should be tendered are identified. Secondly, the optimal offer amount depends on the form of payment and the level of perceived risk. In a share-only offer the amount is constant regardless of risk, whilst if cash is included an increase in risk will imply a decrease in the optimal amount of cash offered. The Nash-Kalai model incorporating shared uncertainty is empirically tested on the same data set used previously. This allows a comparison with earlier results and estimation of the extent of the uncertainty. An extension of this model is proposed, incorporating an alternative form of the utility functions. The second part of the thesis makes use of ideas from negotiation analysis to construct a dynamic model of the complex processes involved in negotiation. It offers prescriptive advice to one of the players on likely Pareto-optimal bargaining strategies, given a description of the strategy the other party is likely to employ. The model describes the negotiating environment and each player's negotiating strategy in terms of a few simple parameters. The model is implemented via a Monte Carlo simulation procedure, which produces expected gains to each player and average transaction values for a wide range of each of the players' strategies. The resulting two-person game bimatrix is analysed to offer general insights into negotiated outcomes, and using conventional game-theoretic and Bayesian approaches to identify "optimal" strategies for each of the players. It is shown that for the purposes of identifying optimal negotiating strategies, the players strategies (described by parameters which are continuous in nature) can be adequately approximated by a sparse grid of discrete strategies, providing that these discrete strategies are chosen so as to achieve an even spread across the set of continuous strategies. A sensitivity analysis on the contextual parameters shows that the optimal strategy pair is very robust to changes to the negotiating environment, and any such changes that have the players start negotiating from positions more removed from one another is more detrimental to the target. A conceptual decision support system which uses the model and simulated results as key components is proposed and outlined.
Open Access
Investigating efficiency in the emergency department at Groote Schuur Hospital
(2010) Mowbray, Allister; Stewart, Theodor J
Includes bibliographical references (p. 92-93).
Open Access
Mathematical modelling and risk management in deregulated electricity markets
(2005) Davis, Stephen; Stewart, Theodor J
In this thesis we aim to explore how electricity generation companies cope with the transition to a competitive environment in a newly deregulated electricity industry. Analyses and discussions are generally performed from the perspective of a Generator/Producer, otherwise they are undertaken with respect to the market as a whole. The techniques used for tackling the complex issues are diverse and wide-ranging as ascertained from the existing literature on the subject. The global ideology focuses on combining two streams of thought: the production optimisation and equilibrium techniques of the old monopolistic, cost-saving industry and; the new dynamic profit-maximising and risk-mitigating competitive industry. Financial engineering in a new and poorly understood market for electrical power must now take place in conjunction with - yet also constrained by - the physical production and distribution of the commodity.
Open Access
Modeling of Spaza shop operations using soft and hard operational research techniques
(2009) Sabwa, Jean-Marie; Stewart, Theodor J
Globalization has transformed the world into a big village in which the rich are becoming richer and the poor getting poorer. In the commercial world the trend is for big business to buy out the smaller companies and consequently get bigger. Yet it is arguable that small businesses have assisted in providing much needed services to small communities that occupy informal settlements and exist on or below the poverty datum line. The South African government has amongst its main objectives the alleviation of poverty and the improvement of life in previously disadvantaged communities. The government has allowed the micro-enterprises and small businesses in the informal sector to thrive and in this sector are Spaza shops that supply a wide range of grocery commodities to informal settlements. This paper is about an application framework of soft and hard operational research (OR) techniques used to address the performance of micro-enterprises with Spaza shops in Western Cape as a speciﬁc case study. The techniques include Strategic Options Development and Analysis (SODA) using Causal mapping and Soft System Methodology (SSM). These were chosen because of their suitability to understand performance problems faced by Spaza shops owners and ﬁnd ways of improving the current situation by modelling the intervention of stakeholders. The improvement of Spaza shop businesses is a matter for all stakeholders. Causal mapping, helped to identify and structure the multiple conﬂicting aspects of Spaza shops business. Soft System Methodology made it possible to conceptualize the intervention model based on the rich picture and root deﬁnitions for relevant world-views and see what changes are culturally feasible and systematically desirable. Computer simulations were used to help design and test performance measurement indicators for the Spaza shops so as to enable decision-makers to choose the optimal strategy. Statistical analysis came into account to enable us to capture the seasonality and bring up clustering patterns.
Open Access
Multi-objective optimisation under deep uncertainty
(2018) Shavazipour, Babooshka; Stewart, Theodor J
Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision. Furthermore, in some of these problems, the probability and/or the number of different outcomes are also unknown (named deep uncertainty). Therefore, all the probability-based approaches (such as stochastic programming) are unable to address these problems. On the other hand, involving various stakeholders with different (possibly conflicting) criteria in the problems brings additional complexity. The main aim and primary motivation for writing this thesis have been to deal with deep uncertainty in Multi-Criteria Decision-Making (MCDM) problems, especially with long-term decision-making processes such as strategic planning problems. To achieve these aims, we first introduced a two-stage scenario-based structure for dealing with deep uncertainty in Multi-Objective Optimisation (MOO)/MCDM problems. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference (a component of what we term the meta-criteria) to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation, which fitted naturally into the broader multi-objective problem context. To aggregate these objectives of the problem, the Generalised Goal Programming (GGP) approach is used. Due to the capability of this approach to handle large numbers of objective functions/criteria, the GGP is significantly useful in the proposed framework. Identifying the goals for each criterion is the only action that the Decision Maker (DM) needs to take without needing to investigate the trade-offs between different criteria. Moreover, the proposed two-stage framework has been expanded to a three-stage structure and a moving horizon concept to handle the existing deep uncertainty in more complex problems, such as strategic planning. As strategic planning problems will deal with more than two stages and real processes are continuous, it follows that more scenarios will continuously be unfolded that may or may not be periodic. "Stages", in this study, are artificial constructs to structure thinking of an indefinite future. A suitable length of the planning window and stages in the proposed methodology are also investigated. Philosophically, the proposed two-stage structure always plans and looks one step ahead while the three-stage structure considers the conditions and consequences of two upcoming steps in advance, which fits well with our primary objective. Ignoring long-term consequences of decisions as well as likely conditions could not be a robust strategic approach. Therefore, generally, by utilising the three-stage structure, we may expect a more robust decision than with a two-stage representation. Modelling time preferences in multi-stage problems have also been introduced to solve the fundamental problem of comparability of the two proposed methodologies because of the different time horizon, as the two-stage model is ignorant of the third stage. This concept has been applied by a differential weighting in models. Importance weights, then, are primarily used to make the two- and three-stage models more directly comparable, and only secondarily as a measure of risk preference. Differential weighting can help us apply further preferences in the model and lead it to generate more preferred solutions. Expanding the proposed structure to the problems with more than three stages which usually have too many meta-scenarios may lead us to a computationally expensive model that cannot easily be solved, if it all. Moreover, extension to a planning horizon that too long will not result in an exact plan, as nothing in nature is predictable to this level of detail, and we are always surprised by new events. Therefore, beyond the expensive computation in a multi-stage structure for more than three stages, defining plausible scenarios for far stages is not logical and even impossible. Therefore, the moving horizon models in a T-stage planning window has been introduced. To be able to run and evaluate the proposed two- and three-stage moving horizon frameworks in longer planning horizons, we need to identify all plausible meta-scenarios. However, with the assumption of deep uncertainty, this identification is almost impossible. On the other hand, even with a finite set of plausible meta-scenarios, comparing and computing the results in all plausible meta-scenarios are hardly possible, because the size of the model grows exponentially by raising the length of the planning horizon. Furthermore, analysis of the solutions requires hundreds or thousands of multi-objective comparisons that are not easily conceivable, if it all. These issues motivated us to perform a Simulation-Optimisation study to simulate the reasonable number of meta-scenarios and enable evaluation, comparison and analysis of the proposed methods for the problems with a T-stage planning horizon. In this Simulation-Optimisation study, we started by setting the current scenario, the scenario that we were facing it at the beginning of the period. Then, the optimisation model was run to get the first-stage decisions which can implement immediately. Thereafter, the next scenario was randomly generated by using Monte Carlo simulation methods. In deep uncertainty, we do not have enough knowledge about the likelihood of plausible scenarios nor the probability space; therefore, to simulate the deep uncertainty we shall not use anything of scenario likelihoods in the decision models. The two- and three-stage Simulation-Optimisation algorithms were also proposed. A comparison of these algorithms showed that the solutions to the two-stage moving horizon model are feasible to the other pattern (three-stage). Also, the optimal solution to the three-stage moving horizon model is not dominated by any solutions of the other model. So, with no doubt, it must find better, or at least the same, goal achievement compared to the two-stage moving horizon model. Accordingly, the three-stage moving horizon model evaluates and compares the optimal solution of the corresponding two-stage moving horizon model to the other feasible solutions, then, if it selects anything else it must either be better in goal achievement or be robust in some future scenarios or a combination of both. However, the cost of these supremacies must be considered (as it may lead us to a computationally expensive problem), and the efficiency of applying this structure needs to be approved. Obviously, using the three-stage structure in comparison with the two-stage approach brings more complexity and calculations to the models. It is also shown that the solutions to the three-stage model would be preferred to the solutions provided by the two-stage model under most circumstances. However, by the "efficiency" of the three-stage framework in our context, we want to know that whether utilising this approach and its solutions is worth the expense of the additional complexity and computation. The experiments in this study showed that the three-stage model has advantages under most circumstances(meta-scenarios), but that the gains are quite modest. This issue is frequently observed when comparing these methods in problems with a short-term (say less than five stages) planning window. Nevertheless, analysis of the length of the planning horizon and its effects on the solutions to the proposed frameworks indicate that utilising the three-stage models is more efficient for longer periods because the differences between the solutions of the two proposed structures increase by any iteration of the algorithms in moving horizon models. Moreover, during the long-term calculations, we noticed that the two-stage algorithm failed to find the optimal solutions for some iterations while the three-stage algorithm found the optimal value in all cases. Thus, it seems that for the planning horizons with more than ten stages, the efficiency of the three-stage model be may worth the expenses of the complexity and computation. Nevertheless, if the DM prefers to not use the three-stage structure because of the complexity and/or calculations, the two-stage moving horizon model can provide us with some reasonable solutions, although they might not be as good as the solutions generated by a three-stage framework. Finally, to examine the power of the proposed methodology in real cases, the proposed two-stage structure was applied in the sugarcane industry to analyse the whole infrastructure of the sugar and bioethanol Supply Chain (SC) in such a way that all economics (Max profit), environmental (Min CO₂), and social benefits (Max job-creations) were optimised under six key uncertainties, namely sugarcane yield, ethanol and refined sugar demands and prices, and the exchange rate. Moreover, one of the critical design questions - that is, to design the optimal number and technologies as well as the best place(s) for setting up the ethanol plant(s) - was also addressed in this study. The general model for the strategic planning of sugar- bioethanol supply chains (SC) under deep uncertainty was formulated and also examined in a case study based on the South African Sugar Industry. This problem is formulated as a Scenario-Based Mixed-Integer Two-Stage Multi-Objective Optimisation problem and solved by utilising the Generalised Goal Programming Approach. To sum up, the proposed methodology is, to the best of our knowledge, a novel approach that can successfully handle the deep uncertainty in MCDM/MOO problems with both short- and long-term planning horizons. It is generic enough to use in all MCDM problems under deep uncertainty. However, in this thesis, the proposed structure only applied in Linear Problems (LP). Non-linear problems would be an important direction for future research. Different solution methods may also need to be examined to solve the non-linear problems. Moreover, many other real-world optimisation and decision-making applications can be considered to examine the proposed method in the future.
Open Access
Multi-objective optimization techniques in electricity generation planning
(2011) Tuyiragize, Richard; Stewart, Theodor J; Luboobi, Livingstone S
The objective of this research is to develop a framework of multi-objective optimization (MOO) models that are better capable of providing decision support on future long-term electricity generation planning (EGP), in the context of insufficient electricity capacity and to apply it to the electricity system for a developing country. The problem that motivated this study was a lack of EGP models in developing countries to keep pace with the countries' socio-economic and demographic dynamics. This research focused on two approaches: mathematical programming (MP) and system dynamics (SD). Detailed model descriptions, formulations, and implementation results are presented in the thesis along with the observations and insights obtained during the course of this research.
Open Access
An object-oriented approach to structuring multicriteria decision support in natural resource management problems
(2001) Liu, Dingfei; Stewart, Theodor J
The undertaking of MCDM (Multicriteria Decision Making) and the development of DSSs (Decision Support Systems) tend to be complex and inefficient, leading to low productivity in decision analysis and DSSs. Towards this end, this study has developed an approach based on object orientation for MCDM and DSS modelling, with the emphasis on natural resource management. The object-oriented approach provides a philosophy to model decision analysis and DSSs in a uniform way, as shown by the diagrams presented in this study. The solving of natural resource management decision problems, the MCDM decision making procedure and decision making activities are modelled in an object-oriented way. The macro decision analysis system, its DSS, the decision problem, the decision context, and the entities in the decision making procedure are represented as "objects". The object-oriented representation of decision analysis also constitutes the basis for the analysis ofDSSs.
Open Access
Planning for the strategic management of South Africa's West Coast rock lobster fishery : an integrated approach to group decision support
(1998) Malyon, Brett Edwin; Stewart, Theodor J
As Bryson (1995) points out, strategic planning is particularly useful for assisting organisations and communities to deal with change. This study was carried out at a time of great change in South Africa, when a new fisheries policy was being formulated and negotiated. The research describes an intervention with a group of .fisheries managers, scientists, fishing company directors and other key stakeholders, in planning for the future management of the West Coast Rock Lobster fishery. The primary objective of the study was to consider an integrated approach to group decision support, incorporating a particular soft-OR approach, SODA, together with multi-criteria decision analysis (MCDA). An integration of these two approaches has recently been suggested by researchers, for several reasons. Firstly, different phases of an intervention usually involve different tasks. Secondly, mixing methodologies will enable different aspects of the problem to be modelled and analysed. SODA was used at the outset, for divergent exploration and structuring of the problems surrounding the development of an operational management procedure (OMP) for the fishery, including more subjective and qualitative information. Several stakeholder groups opposed the idea of an OMP in the form in which it was proposed.
Open Access
The racetrack : a scientific approach
(1993) Kreel, Larry; Stewart, Theodor J
Horseracing and its associated activity of gambling invites academic research of a multidisciplinary nature. Economics, psychology, mathematics and statistics are all fields that have investigated the two topics. In 1976 economists discovered a new body of data on which they could test their theories. For many years psychologists have investigated human behaviour in gambling situations. Mathematicians have developed optimal betting strategies. Statisticians have assisted in all the investigations as well as utilised decision theory, probability theory and regression analysis, in their own right, within the discipline. Why do academics devote their time to this subject? The furthering of knowledge in general in the above fields is important. Also, because the possibility of making money with relatively little work exists, people from all walks of life will be drawn to the intellectual challenge of finding winners. Researchers know that in order to derive money making systems, research on an academic scale is necessary. The amount of data available is phenomenal and although much of it is utilized by the public, some of it is not and that which is, is not always used in a consistent manner. The research in this work concentrates on all four fields mentioned above. A general, overview of the work done in each section is as follows. In chapters two and three, the betting market is examined within the framework of the efficient markets hypothesis. Tests of the three well known forms of efficiency are performed. In chapter four, within the framework of the expected utility hypothesis, the behaviour of gamblers is analysed. The investigation concentrates on behaviour observed at the racetrack, but draws ideas from other gambling situations as well. In chapter five, an investigation is made into horseraces, considering a race to be a sports event. This will consider the competing horses as athletes and will try and identify which fundamental factors are most important in determining the victor of such a race. In chapter six, some statistical theory, which has simple applications in horseracing is examined. In chapter seven, the economics of racetrack management is investigated.
Open Access
Robustness analysis based on weight restrictions in data envelopment analysis
(2006) Kantu, Dieudonne Kabongo; Stewart, Theodor J
Evaluating the performance of organisations is essential to good planning and control. Part of this process is monitoring the performance of organisations against their goals. The comparative efficiency of organizations using common inputs and outputs makes it possible for organizations to improve their performance so that can operate as the most efficient organizations. Resources and outputs can be very diversified in nature and it is complex to assess organizations using such resources and outputs. Data Envelopment Analysis models are designed to facilitate this of assessment and aim to evaluate the relative efficiency of organisations. Chapter 2 is dedicated to the basic Data Envelopment Analysis. We present the following: * A review of the Data Envelopment Analysis models; * The properties and particularities of each model. In chapter 3, we present our literature survey on restrictions. Data Envelopment Analysis is a value-free frontier which has the of yielding more objective efficiency measures. However, the complete freedom in the determination of weights for the factors and products) relevant to the assessment of organisations has led to some problems such as: zero-weights and lack of discrimination between efficient organizations. Weight restriction methods were introduced in order to tackle these problems. The first part of chapter 3 in detail the motivations for weight restrictions while the second part presents the actual weight restriction rnethods.
Open Access
Selection of multicriteria decision making methodologies in scenario based planning
(1995) Heynes, Wynford Gustav; Stewart, Theodor J
This dissertation investigates the application of Multicriteria Decision Making (MCDM) methodologies to the area of scenario based policy planning. We examine how the tools of MCDM can be used to develop a Decision Support System (DSS) that would allow management or policy planners to resolve conflicting goals and interests. Ideally, the resolution would be obtained by the various decision makers (DMs) in such a manner, that it satisfies all the relevant interest groupings at a maximum level of achievement for all concerned. This is not always possible and compromises need to be made that are fair and equitable to all the relevant interests. Stewart et al. (1993), in a report entitled: "Scenario Based Multicriteria Policy Planning for Water Management in South Africa", develop the principles of a procedure for implementing scenario based multicriteria policy planning. Their iterative procedure is illustrated in figure 2.1, chapter 2, of this paper. In this dissertation, we refine certain parts of this procedure and the two areas in particular that we have looked at are: (1) filtering a large set of policy scenarios (Background Set), that could be a continuum, to form a smaller set (Foreground Set), and (2) further reducing the smaller set to form a solution set of policy scenarios. (The generic terms "Background Set" and "Foreground Set" are defined in section 2.1 of chapter 2.) The main objectives of this study were therefore mainly twofold and are as follows: (1) to determine what MCDM methods are relevant to natural resources management (using water as a case study), and (2) to investigate how these methods need to be adopted for use in an interactive DSS. We address the first objective by surveying the literature in an attempt to identify potential MCDM approaches that are suitable to (i) reduce a large set of alternatives, analogous to the Background Set, to a more manageable smaller set, analogous to the Foreground Set of alternatives, and (ii) refine this Foreground Set in order to present the DMs with a solution set of alternatives from which University of Cape Town they will make their final selection. The literature has until now not dealt explicitly with these two issues and we had to adapt certain MCDM approaches, many of which have been developed in a linear programming context, to suit our purposes.
Open Access
A simulation model of antimalarial drug resistance
(2009) Silal, Sheetal Prakash; Little, Francesca; Stewart, Theodor J
Malaria ranks among the world's most important tropical parasitic diseases with world prevalence figures between 350 and 550 million clinical cases per annum. [WHO, 2008a] 'Treatment and prevention of malaria places a considerable burden on struggling economies where the disease is rampant. Research in malaria does not stop as the change in response to antimalarial drug treatment requires the development of new drugs and innovation in the use of old drugs. This thesis focused on building a model of the spread of resistance to Sulfadoxine/Pyrimethamine (SP) in a setting where both SP and SP in artemisinin-based combination therapy (ACT) are the first line therapies for malaria. The model itself is suitable to any low transmission setting where antimalarial drug resistance exists but the country of choice in this modeling exercise was Mozambique. The model was calibrated using parameters specific to the malaria situation in Mozambique. This model was intended to be used to aid decision making in countries where antimalarial drug resistance exists to help prevent resistance spreading to such an extent that drugs lose their usefulness in curing malaria. The modeling technique of choice was differential equation modeling; a simulation technique that falls under the System Dynamics banner in the Operations Research armamentarium. It is a technique that allowed the modeling of stocks and flows that represent different stages or groupings in the disease process and the rate of movement between these stages respectively. The base model that was built allowed infected individuals to become infectious, to be treated with SP or ACT and to be sensitive to or fail treatment. Individuals were allowed a period of temporary immunity where they would not be reinfected until the residual SP had been eliminated from their bloodstream. The base model was then further developed to include the pharmacokinetic properties of SP where individuals were allowed to be reinfected with certain strains of infection given the level of residual drug in their bloodstream after their current infection had been cleared. The models used in this thesis were built with idea of expanding on previous models and using available data to improve parameter estimates. The model at its core is similar to the resistance model used in Koella and Antia [2003] where differential equation modeling was used to monitor a population as it became infected with a sensitive or resistant infection and then University of Cape Town recovered. The inclusion in the model of the PK component was derived from Prudhomme-O'Meara et al. [2006] where individuals could be reinfected depending on the residual drug in their bloodstream. Rather than modeling simply sensitive and resistant infections, mutations categories were used as was the case in Watkins et al. [2005] population genetics model. The use of mutation categories allowed one to use parameters specific to these categories rather than the sensitive/resistant stratification and this is particularly relevant in Mozambique where all mutation categories still exhibit some degree of sensitivity to treatment i.e. total resistance has not yet developed for any particular mutation category. The last adaptation of the model was to use gametocyte information directly to determine human infectiousness rather than through using a gametocyte switching rate (constant multiplier used to convert parasite density to gametocyte density) as was done in Pongtavompinyo [2006]. The models developed in this thesis found that the existing vector control and drug policy in Mozambique had the major effect of decreasing total prevalence of malaria by approximately 70% in the 11 year period. The distribution of Res3 (presence of DHFR triple) and Res5 (presence of DHFR triple and DHPS double) infections changed over the 11 year period with Res3 infections initially increasing and then decreasing while Res5 infections started low and increased to overtake Res3 infections. The timing of the change in this composition of infection corresponds with the introduction of ACT and thus it appears that the use of ACT prompted the increased prevalence of quintuple parasites over DHFR triple and sensitive parasites. The total number of failures decreased substantially after the introduction of ACT to 17% of its previous level. The results of the base model corresponded with the observed data from the SEACAT study in terms of the magnitude and the trends of the impact of the change to ACT policy, but underestimated the impact of the vector control strategies compared to rapid effect noted in Sharp et al. [2007]. The Scenario testing of the base model showed that vector control is an effective strategy to reduce prevalence and that it is sensitive to the time at which the control is started as it decreased prevalence very gradually. The Scenario testing of the base model also showed that the introduction of ACT in Mozambique had a greater impact on reducing prevalence and that the start time of the ACT strategy did not decrease the effect on prevalence though earlier start times decreased the total number of resistance cases. The ratio of Res5 to Res3 infections increased faster when ACT was the treatment policy than when SP was the policy. Thus higher values of this ratio are associated with ACT being the treatment strategy in place. Thus differential equation modeling is an effective modeling tool to capture the spread of disease and to test the effects of policy interventions as it allows one to assess these effects on populations and averages out individual-level intricacies to better inform policy decisions.